8253results about "2D-image generation" patented technology

The invention relates to the technical field of image detection, in particular to a PCBA board defect detection method and system based on image processing, and the method comprises the following steps: carrying out the meshing calculation of a gray scale deviation after a gray scale image is subjected to Gaussian filtering denoising, generating change rate data, carrying out the statistics of a frequency number, constructing a histogram, combining with an Otsu algorithm, and generating a candidate mask; extracting pixels based on a mask, calculating a gradient modulus, screening edge candidate points, carrying out gradient direction connection and morphological processing to generate a complete edge structure, expanding a connected domain through a region growing algorithm, aligning the connected domain with a template contour, and outputting defect coordinates. According to the method, the defect identification sensitivity is improved through combination of gray level image gridding processing and dynamic threshold calculation, a candidate mask is generated through grid gray level change rate statistics and an Otsu algorithm to avoid over-segmentation missing detection, and the contour precision is improved through combination of gradient modulus difference screening and morphological closed operation optimization. The region growing algorithm and template dynamic alignment reduce deformation misjudgment, and staged dimension reduction and feature enhancement reduce calculation complexity and solve resource waste.

The invention discloses an AI-based animation split script automatic generation and visual preview method and system, and the method comprises the following steps: 1, receiving a natural language script text inputted by a user, the natural language script text comprising scene description, role action, dialogue and shot indication information; step 2, performing semantic analysis and structured analysis on the script text based on a natural language processing technology, and identifying and extracting key narrative elements; by introducing an artificial intelligence technology, end-to-end automatic generation and interactive optimization from a character script to a dynamic split rehearsal video are realized, the system can deeply understand scenes, actions, role emotions and shot languages in the script, corresponding visual elements are automatically matched and generated, and the dynamic split rehearsal effect is improved. And the timeline and the rhythm conforming to the film and television grammar are constructed, so that the efficiency and the consistency of the split creation are greatly improved, and the professional threshold and the manufacturing cost are reduced.

The present invention sets forth techniques for performing style transfer from multiple supplied style images to a supplied content image to generate novel images that include style elements from the multiple supplied style images and content elements from the supplied content image. The techniques include guiding one or more self-attention and cross-attention layers included in a machine learning model based on the multiple supplied style images, such that content elements and style elements included in the style images are not entangled when generating the novel images. The techniques also distill a small subset of representative attention map values from multiple style images, improving performance while reducing computational costs compared to processing all attention map values from the multiple style images.

The invention relates to the technical field of unmanned aerial vehicle control, provides a visual environment generation method, system and device based on a neural radiation field and a storage medium, and solves the problem of poor visual environment generation effect. The method comprises the steps that semantic segmentation is carried out on a corrected image, image data with semantic tags are generated, and the semantic tags are used for marking categories of objects in an outdoor scene; updating the neural radiation field model based on the image data with the semantic tag, mapping the semantic tag to a voxel feature space corresponding to the neural radiation field model, and constructing a semantic point cloud; identifying a non-key area in the multi-view image, performing redundant voxel cutting on the non-key area, and generating a cut semantic point cloud; and utilizing the clipped semantic point cloud to drive a neural radiation field model, and generating any visual angle scene of the outdoor scene. According to the technical scheme, lightweight semantic modeling and efficient free viewpoint rendering of the outdoor visual environment are achieved, and the new visual angle generation efficiency and quality of the complex environment are improved.

The invention relates to the technical field of medical image processing, in particular to an artificial intelligence assisted intraoperative imaging method, which comprises the following steps: S1, preprocessing a multi-modal medical image, segmenting and recognizing an anatomical structure by a deep learning model according to the preprocessed image, measuring anatomical parameters based on a segmentation and recognition result, and generating an operation planning path by artificial intelligence according to the anatomical parameters; s2, collecting a C-shaped arm perspective image stream in real time, dynamically tracking space coordinates of a surgical instrument, comparing the position of the instrument with a surgical planned path, calculating offset, and when the offset is greater than an offset threshold, outputting correction guidance through an AR superposition layer; and S3, monitoring an image quality index in real time, dynamically adjusting exposure parameters through a reinforcement learning model, and when a metal implant is detected, switching a dual-energy-spectrum mode and executing an artifact suppression algorithm. According to the method, preoperative precise planning and intraoperative assistance are realized through artificial intelligence, the problems of poor image quality and high radiation risk are solved through technical optimization, and the method has important clinical application value.

Computer-implemented method of training a machine learning system for generating images from a predefined layout. The machine learning system is a diffusion probabilistic model with an encoder part and a decoder part, wherein a normalization layer in a residual block of the decoder part comprises weighted layout-aware affine transformation parameters γ and β, determined from layout-aware affine transformation parameters γ' and β' by multiplication with a weighted semantic map, wherein the weighted semantic map comprises a sum of two contributions, wherein the first contribution comprises a non-overlapping semantic map computed from determined and size-ranked object probabilistic masks and wherein the second contribution comprises an edge-aware semantic map, determined from extended object probabilistic masks, wherein an extended object probabilistic mask is extended with respect to the corresponding object probabilistic mask by one pixel along the borders.

The invention discloses an MRI-PET mode conversion method and system based on a generative adversarial network, and belongs to the technical field of artificial intelligence medical image generation. And the multi-scale structure representation injection module injects multi-scale anatomical prior information at different stages of the encoder, and overcomes the limitations of insufficient utilization of prior information and single injection scale. And the adaptive semantic residual fusion module adopts semantic attention guidance and double-branch attention weighting, adaptively fuses fine-grained local features and global context information, harmonizes the difference between the fine-grained local features and the global context information in an abstract level and a semantic category, and solves the problems of feature conflict and semantic fuzziness in a bottleneck region. The direction sensing space-frequency discriminator realizes multi-dimensional and fine-grained adversarial supervision through a space, frequency and local image block multi-branch collaborative discrimination mechanism, and improves the structural fidelity and spectrum authenticity of a synthetic image. And the generated image is superior to the existing method in indexes such as structural similarity and peak signal-to-noise ratio, and has higher clinical practical value.

The present invention relates to the field of rock structure measurement, and disclosed are a three-dimensional pore reconstruction method and system based on a rock image, for use in solving the problem that using a machine learning method to reconstruct pores in a rock image requires relatively high mathematical or computer science expertise, involves high labor and economic costs, and is difficult to apply to small-scale rock pore reconstruction projects. The method comprises: step 1: grouping slice images of each rock sample into one set, and performing preprocessing; step 2: performing grayscale processing, histogram equalization and normalization processing on an image; step 3: using a Harris corner detection algorithm to perform corner detection, sorting Harris response values, selecting key points by setting the number of key points, and using a non-maximum suppression method to screen the selected key points; step 4: segmenting the boundaries of pores in the image by means of an adaptive threshold selection method, and extracting pore features; and step 5: using a marching cubes algorithm to construct a three-dimensional pore reconstruction model. The present invention is used for three-dimensional reconstruction of pores in a rock image.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for content generation are provided. One of the methods includes receiving one or more user input identifying information associated with one or more media elements and one or more characteristics of the video to be generated; and generating video content based on the received one or more user inputs, the generating comprising: identifying assets to include in the video, the assets including an avatar, generating a script for the video, and assembling a video layout.

The invention relates to the technical field of medical image processing, in particular to a CT image intelligent analysis system for pneumonia auxiliary screening. The method comprises the following steps: firstly, preprocessing a chest CT image and detecting a candidate focus area; secondly, extracting a topological feature, a deep convolution feature and a texture statistical feature based on a persistent coherence theory from each candidate focus, and performing feature fusion through a multi-head self-attention mechanism to generate a unified focus representation vector; mapping the lesion characterization vectors to a pre-constructed radiology knowledge graph, adopting a graph neural network for reasoning, and outputting the pneumonia suspected probability and lesion classification of each lesion; and finally, performing fusion and uncertainty quantification on the analysis results of the plurality of focuses by adopting an evidence theory, and generating a comprehensive screening report. According to the method, complex-form lesions are effectively identified through topological features, accurate identification of lesion types is realized through knowledge graph reasoning, and diagnosis uncertainty quantification is provided through an evidence theory.

The invention belongs to the technical field of image processing, and particularly relates to a time sequence frequency domain feature enhanced diffusion-based sea temperature image completion method and system, and the method comprises the following steps: inputting a damaged sea temperature image, an initialized cloud mask, a weekly average sea temperature image and a historical sequence sea temperature image into a time sequence frequency domain feature extraction module for processing; and mapping into fused frequency domain features, and outputting a complex frequency domain condition vector. A real SST image on the current day is coded into an initial latent variable through a latent space enhancement diffusion module, a complete noisy latent variable is generated through forward diffusion sampling, a denoised latent variable is obtained through backward stable diffusion sampling, after the denoised latent variable is decoded into a pixel field through an output reconstruction module, constraint post-processing is carried out in combination with a mask and a damaged sea temperature image, and a real SST image is obtained. And finally outputting a reconstructed image. And the damaged sea temperature image completion precision and the time sequence continuity are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a visual token generation method, device, equipment and medium based on a shared index, and the method comprises the steps: obtaining an input image, and extracting semantic features and pixel features through a semantic encoder and a pixel encoder; calculating the distance between each feature and a codebook thereof, and carrying out weighted summation to determine a shared index; retrieving quantitative features from the codebook using a shared index; respectively generating a reconstructed image and a reconstructed semantic feature by using a pixel decoder and a semantic decoder; jointly optimizing an encoder, a codebook and a decoder based on a reconstruction result; and generating a unified visual token sequence for the target task image by using the optimized component. Through double-flow feature extraction, shared mapping quantization and joint loss optimization, global semantic information and local pixel details can be reserved in the visual token at the same time, so that the model has accurate understanding ability, high-fidelity images can be generated, and the performance of understanding and generating tasks is improved.

Examples described herein relate to personalized image generation using combined image features. A plurality of input images is provided by a user of an interaction application. Each of the plurality of input images depicts at least part of a subject. Each input image is encoded to obtain an identity representation. The identity representations obtained from the plurality of input images are combined to obtain a combined identity representation associated with the subject. A personalized output image is generated via a generative machine learning model. The generative machine learning model processes the combined identity representation and at least one additional image generation control to generate the personalized output image. At a user device, the personalized output image is presented in a user interface of the interaction application.

Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for training a denoising neural network and, once the denoising neural network is trained, generating new images using the denoising neural network. In particular, the described techniques include obtaining data specifying a trained initial denoising neural network, obtaining training data, and training, using the training data and on a denoising objective, a target denoising neural network. The target denoising neural network includes the core-subnetwork of the trained initial denoising neural network, but also includes a target encoder and decoder neural networks that are not included in the trained initial denoising neural network. By training a target neural network that includes the core-subnetwork of a trained initial denoising neural network, the system achieves stable training of a large-scale target denoising neural networks that can be used to generate high resolution images.

In some embodiments, a method receives a text prompt. A text encoder is executed on the text prompt to generate a representation. The method generates a set of images based on the representation and a set of parameters of an image generation model. The set of images is ranked using reward values that are generated by a reward model. The reward model is trained using human input that provided feedback on a quality of generated images using the image generation model. The method outputs one or more images based on the ranking in response to the text prompt.

The present invention provides a system and method for generating proposals for infrastructure modalities, such as electrical substations, using advanced artificial intelligence. It includes an input interface for data collection, a lightweight generative or rendering pipeline for creating preliminary 2D designs, and a generative model selected from diffusion, transformer-based, GAN or other architectures for refining these into detailed 3D models and generating preliminary designs. The system evaluates designs against predefined criteria to ensure compliance and feasibility. Supported by a cloud-based infrastructure for robust data processing and integration with third-party services, this system enhances the efficiency, accuracy, and compliance of modality planning and proposal generation.

The present disclosure describes techniques for processing monocular videos using three-dimensional gaussian splatting (3DGS). Spatial decomposition and temporal decomposition are performed on a monocular video to generate a plurality of clips. A first set of 3DGS representing foreground objects in each of the plurality of clips are initialized and optimized. A second set of 3DGS representing background in each of the plurality of clips are initialized and optimized. Two images are generated for each frame comprised in each of the plurality of clips based on the first set of 3DGS and the second set of 3DGS, respectively. Two images are merged to generate a resulting image for each frame in each of the plurality of clips. The resulting image accurately represents a corresponding frame in the monocular video.

The present invention belongs to the technical field of crop mapping based on remote-sensing images, and relates to a method and system for crop mapping across large regions with low sample dependence. The method includes: acquiring remote sensing data, ground sample data, meteorological data, soil data, establishing geographically divided crop planting regions; establishing key growth period model libraries corresponding to individual crop regions; constructing machine learning models based on a plurality of machine learning algorithms, to obtain machine learning crop extraction models; selecting an optimal machine learning crop extraction model; acquiring a spatial crop distribution base map; performing product correction based on the disaster information; and acquiring a regional crop map using a target crop extraction model adapted for the disaster response. The present invention, achieve high-accuracy and large-scale crop mapping, and reduce the crop sample dependence of crop mapping.

The invention discloses a diversified epiphyseal development map generation method and device based on a potential diffusion model. The method comprises the following steps: making a data set, covering and collecting an original X-ray film image, preprocessing data, labeling a skeleton maturity level and desensitizing the image; an original image is encoded to a potential space by using a variational auto-encoder, and the image detail generation capability is improved by optimizing a loss function; a fuzzy learning module is introduced in the de-noising stage of the diffusion model, and the diversity of generated skeleton features is enhanced through operations such as batch normalization and random disturbance; a progressive alignment strategy is adopted, skeleton basic structure learning is emphasized in the initial stage, a skeleton grade evaluation network is introduced in the later stage, and skeleton detail features are emphasized; optimizing the generation model by using the skeleton recognition model; and inputting development levels, ages and genders of 14 skeletons, and generating a corresponding left-hand skeleton X-ray film image. According to the method, diversified and high-quality skeletal development images are generated through a potential diffusion model framework in combination with VAE optimization, an FLM module and a progressive alignment strategy, skeletal development features are truly reflected, the problems of detail loss and insufficient diversity in the prior art are solved, the method is particularly excellent in performance when sparse data and long-tail distribution are processed, and the method is suitable for large-scale popularization and application. And the authenticity of the generated image and the practicability of medical diagnosis are improved.

The invention relates to an automatic driving key test scene generation method fusing a visual language large model and a diffusion model, and the method comprises the steps: constructing a diffusion model which is used for learning the data distribution of natural driving; aiming at an original scene, utilizing a visual language large model to analyze and form a guide strategy; and based on a guiding strategy and a guiding diffusion algorithm, guiding intervention is carried out on the diffusion model in a simulation environment so as to calculate a background vehicle track and generate a key scene. Compared with the prior art, the method has the advantages that the original scene is automatically analyzed and potential risks are inferred by utilizing the visual language large model, so that the generation process of the bottom diffusion model is guided, intervened and corrected, and the key test scene with high interactivity, high risk and authenticity can be generated in closed-loop simulation.

The invention discloses an image processing method and device, equipment and a storage medium, and is applied to the technical field of image processing. According to the scheme, the method comprises the following steps: detecting angular points in a target image to be interpolated based on an angular point detection algorithm to obtain feature points in the target image; for the texture complexity of the neighborhood image of each feature point, selecting a target interpolation algorithm for processing the feature point; and performing interpolation processing on the feature points based on a target interpolation algorithm to generate an interpolation result image. For angular points with complex textures, a high-precision interpolation algorithm is adopted to ensure that image details are finely restored; and for the angular points with slightly simple textures, a low-calculation-amount algorithm is used to reduce resource occupation, so that the limitation that a single algorithm is uniformly adopted for the whole image in the prior art is avoided, and the redundant calculation amount is remarkably reduced while the interpolation precision of the key feature region is ensured and the quality of the whole image is maintained.

A method comprising generating image descriptions of images in an original training set of images; determining, using at least one LLM, at least one domain and / or class which is under-represented in the original training set; generating, using a second LLM and based on the determination of the at least one domain and / or class, at least one instruction for a third LLM to generate at least one text prompt; generating, using the third LLM and based on the at least one instruction, the at least one text prompt for a text-to-image model; generating, using the text-to-image model and based on the at least one text prompt, at least one synthetic image; and generating an enhanced training set of images for use in training an image processing machine learning, ML, model, the enhanced training set of images comprising the original training set of images and the at least one synthetic image.

The invention discloses a medical image segmentation method and system based on image-text interaction, and relates to the technical field of image segmentation, and the method comprises the steps: firstly obtaining a medical image, extracting a multi-scale visual feature, carrying out the deep analysis of a user text description through a medical knowledge graph, and carrying out the knowledge enhancement through a medical anatomical knowledge graph; and thus, an enhanced text vector fusing deep semantics and precise anatomical context is constructed. Furthermore, through a multi-granularity semantic grounding and collaborative fusion mechanism, progressive cross-modal alignment and information interaction are carried out on enhanced text vectors and multi-scale visual features, model focusing is guided, a target area is accurately positioned, and finally a high-precision segmentation mask is generated by a decoder. Therefore, the flexibility of the natural language and the accuracy of the medical priori knowledge are combined, the segmentation challenge in a complex or fuzzy scene can be effectively overcome, and the accuracy and robustness of the segmentation task are remarkably improved.

The invention relates to a costume design system and method based on multi-modal AIGC and a storage medium thereof. Comprising a multi-source input processing module used for receiving and processing multiple modal inputs including costume design text description, a costume style drawing, a fabric sample image and user preference data; the clothing feature extraction module is used for extracting clothing structure features and visual style features from the multi-source input; the semantic understanding module is used for carrying out semantic understanding and style analysis on the clothing features; the multi-modal fusion module is used for mapping the clothing feature representations of different modals to a unified semantic space and generating a fusion feature vector; and the style migration module is used for realizing design generation of a specific style based on a small number of garment samples through a LoRA low-rank adaptation technology, and is used for generating a garment design drawing through a multi-stage conditional diffusion model based on the fusion feature vector.

The invention belongs to the field of image processing, and provides a gynecological tumor image processing method and system based on AI multi-modal image analysis, and the method comprises the steps: 1, obtaining an original image of a patient, and obtaining a structure mask and an image frame sequence after period alignment and structure normalization based on the original image; step 2, obtaining a focus mask sequence after structure limitation based on the image frame sequence; step 3, respectively acquiring a modal structure semantic tensor of each image in the image frame sequence, and acquiring a fused semantic feature tensor based on the modal structure semantic tensor; 4, obtaining a final focus mask based on the fused semantic feature tensor and the structure mask; and step 5, obtaining a response visualization graph based on the focus mask. The method is clear in technical structure, coherent in task chain and independent in model interface, has real deployment and continuous evolution capabilities, and is particularly suitable for gynecological image AI auxiliary system scenes under periodic driving.

A symbolic neuroadaptive control system is disclosed for real-time arbitration, consent, and ethical modulation of artificial intelligence agents operating in wearable computing environments. The system integrates multimodal biometric telemetry—including high-resolution EEG signals—with a symbolic kernel that performs logic-driven arbitration over cognitive, emotional, and ethical states. Using Coq-verified invariants and zero-knowledge biometric consent tokens, the system constructs a deterministic symbolic execution graph, gating AI outputs based on internal user states such as trauma, stress, or intentionality. Unlike conventional black-box BCI models, the invention routes EEG-inferred affective-symbolic tokens through a formal ethics layer that enforces real-time interrupt control, utility bounding, and trust verification. The kernel enables AGI systems to defer or modify behavior based on user-state alignment, granting sovereign agency over all downstream actions. This neuro-symbolic architecture redefines the interface between human cognition and intelligent machines, enabling emotionally conscious, morally verifiable, and symbolically transparent AI governance in dynamic, high-stakes contexts.The present invention relates to artificial intelligence and neurotechnology, specifically to a real-time, neuro-symbolic operating system kernel that converts electroencephalography (EEG) signals into structured symbolic data for use in emotional cognition, ethical prioritization, autonomous agent dispatch, and real-time telecommunications routing. The invention bridges brain-computer interface (BCI) inputs with symbolic AI architectures to enable ethically aligned machine response during cognitively or emotionally intense events.

The invention discloses an extreme weather photovoltaic power prediction method, system and device, and a medium. The method comprises the steps of obtaining related data of a power plant and performing first processing; constructing a first neural network to extract spatio-temporal features of the cloud picture, and realizing adaptive classification of extreme weather and non-extreme weather through a double-branch discriminator; guiding a conditional diffusion model to generate a non-extreme weather accurate cloud picture by taking the cloud picture spatial-temporal characteristics as constraint conditions; generating an extreme weather accurate cloud picture based on a cloud picture spatio-temporal feature guidance condition generative adversarial network; constructing a second neural network to capture global and local dynamic change characteristics of photovoltaic power and related meteorological data of the power plant; constructing a cross attention module to fuse the weather accurate cloud picture and the dynamic change features; and inputting the fusion features into a third neural network, and dynamically learning mapping from the fusion features to power. According to the invention, through a cloud picture generation framework and a multi-modal fusion mechanism, the problem of failure of a traditional prediction model in extreme weather is effectively solved.

The invention discloses an ultrasonic positioning microscopic imaging method and system based on joint resolution perception and vision Mangbar, and the method specifically comprises the steps: obtaining and preprocessing a plurality of modal angiography data, generating a microbubble motion sequence in combination with blood flow velocity field simulation data, and constructing a data set; inputting the data set into a mixed hierarchical feature pyramid fusion network, carrying out probability density estimation on a microbubble position based on a real-time multi-scale density field estimation algorithm, and introducing a resolution perception algorithm to enhance microbubble features to obtain a plurality of short-track images; performing dynamic feature decoupling and time-frequency analysis on the short-track image, extracting and encoding velocity component features and spatial-temporal context information of a vascular structure, and constructing a double-branch processing network based on visual Mangbar to obtain images respectively reflecting forward motion contribution and backward motion contribution, and finally generating a high-resolution blood vessel image through image fusion. The method has an important value for improving the efficiency and precision in microvascular imaging.

Certain aspects of the present disclosure provide techniques and apparatus for generating responses to large input prompts using a generative artificial intelligence model. An example method generally includes receiving an input prompt for processing using a generative artificial intelligence model. The input prompt is partitioned into a plurality of sub-prompts based on contextual information associated with tokens in the input prompt. A response to the input prompt is generated using the generative artificial intelligence model based on the plurality of sub-prompts and the contextual information associated with the tokens in the input prompt. The generated response is output.